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learning (ML) methods—including surrogate modelling, feature extraction, and inverse design algorithms Generate synthetic microstructures (based on the open-source OptiMic software) Perform descriptor
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Leibniz-Institute for Plant Genetics and Crop Plant Research | Neu Seeland, Brandenburg | Germany | 12 days ago
have hands-on experience in crop genomics and managing genotyping data. You bring first experience with biostatistics methods, e.g. with mixed-models. You are familiar with data analysis using
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with microstructural features and failure mechanisms Development of models to describe degradation mechanisms and predict component lifetime Presentation of research findings at project meetings
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available on site for the development of suitable radiotracers. One focus of the work is on the use and evaluation of large tomographic data sets to derive parameter data for reactive transport modeling
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trophoblast cells in a small animal model. The project is part of the new Collaborative Research Center (SFB 1713) “Maternal Immune Activation: Causes and Consequences”. We are seeking a highly motivated and
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“Cellular Plasticity in Myeloid Malignancies: From Mechanisms to Therapies”. In this CRC we will focus on myeloid malignancies as a model to dissect the various molecular mechanisms that enable and regulate
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phenomena such as the spread of misinformation or the formation of filter bubbles. For this, we rely on rigorous probabilistic methods to model and analyse the intrinsic complexities of these systems
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Disease Modeling and are to be filled on a fixed-term basis in accordance with § 2 WissZeitVG and § 72 HessHG with the opportunity for own academic qualification at the Institute of Lung Health (ILH
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: Prof. Dr. Steven Travis Waller, Chair of Transport modeling and simulation, and co-supervised by at least one additional professor, plus an international tutor of the CRC Requirements: excellent
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civil/electrical/control engineering or mathematics or related study programs with a solid basis in choice modelling and/or reinforcement learning, with knowledge of MATSim is advantageous. Description